Higher frequency network activity flow predicts lower frequency node activity in intrinsic low-frequency BOLD fluctuations.
Higher frequency network activity flow predicts lower frequency node activity in intrinsic low-frequency BOLD fluctuations.
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DOI：
10.1371/journal.pone.0064466
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Dhamala M
中科院分区：
文献类型：
--
作者：
Bajaj S;Adhikari BM;Dhamala M
大脑在休息状态下保持电和代谢活性。已知在分布的脑区域中相干的功能性磁共振成像（fMRI）的血氧水平依赖（BOLD）信号的低频振荡（LFO）表现出这种活动的特征。然而，这些内在的振荡可能会经历动态变化的时间尺度秒到分钟在休息条件。在这里，使用基于小波变换的时频分析技术，我们研究了默认模式网络的动态性质，从参与者在休息条件下保持视觉固定的内在BOLD信号记录。我们重点关注由后扣带回皮质（PCC）、内侧前额叶皮质（mPFC）、左中颞叶皮质（LMTC）和左角回（LAG）组成的默认模式网络。频谱功率和因果流型的分析表明，固有的LFO经历显着的动态变化，随着时间的推移。将LFO的频率间隔0至0.25 Hz划分为四个间隔慢-5（0.01-0.027 Hz），慢速-4（0.027-0.073 Hz），慢速-3（0.073-0.198 Hz）和慢速-2（0.198-0.25 Hz），我们进一步观察到网络活动的慢-4输入-输出流与慢-5节点活动的显著正线性关系，以及具有慢-4节点活动的网络活动的慢-3输入-输出流。发现与呼吸相关频率（慢-2）相关的网络活动与任何频率间隔中的节点活动都没有关系。我们发现，在慢-3带的节点的净因果流与纤维的数量，从扩散张量成像（DTI）的数据，从其他节点连接到该节点。这些发现意味着所谓的静息状态并不是“完全”处于静息状态，较高频率的网络活动流可以预测较低频率的节点活动，并且网络活动流可以反映潜在的结构连接。
The brain remains electrically and metabolically active during resting conditions. The low-frequency oscillations (LFO) of the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI) coherent across distributed brain regions are known to exhibit features of this activity. However, these intrinsic oscillations may undergo dynamic changes in time scales of seconds to minutes during resting conditions. Here, using wavelet-transform based time-frequency analysis techniques, we investigated the dynamic nature of default-mode networks from intrinsic BOLD signals recorded from participants maintaining visual fixation during resting conditions. We focused on the default-mode network consisting of the posterior cingulate cortex (PCC), the medial prefrontal cortex (mPFC), left middle temporal cortex (LMTC) and left angular gyrus (LAG). The analysis of the spectral power and causal flow patterns revealed that the intrinsic LFO undergo significant dynamic changes over time. Dividing the frequency interval 0 to 0.25 Hz of LFO into four intervals slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz) and slow-2 (0.198–0.25 Hz), we further observed significant positive linear relationships of slow-4 in-out flow of network activity with slow-5 node activity, and slow-3 in-out flow of network activity with slow-4 node activity. The network activity associated with respiratory related frequency (slow-2) was found to have no relationship with the node activity in any of the frequency intervals. We found that the net causal flow towards a node in slow-3 band was correlated with the number of fibers, obtained from diffusion tensor imaging (DTI) data, from the other nodes connecting to that node. These findings imply that so-called resting state is not ‘entirely’ at rest, the higher frequency network activity flow can predict the lower frequency node activity, and the network activity flow can reflect underlying structural connectivity.
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10.1523/jneurosci.1776-08.2008
发表时间：
2008-10-01
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
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2010-11-17
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The Journal of neuroscience : the official journal of the Society for Neuroscience
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